Building Energy Analytics

Acoustic Enabled Fine-Grained Energy Analytics

Fine-grained monitoring of everyday appliances can provide better feedback to the consumers and motivate them to change behavior in order to reduce their energy usage. It also helps to detect abnormal power consumption events, long-term appliance malfunctions and potential safety concerns. Commercially available plug meters can be used for individual appliance monitoring but for an entire house, each such individual plug meters are expensive and tedious to setup. Alternative methods relying on Non-Intrusive Load Monitoring techniques help disaggregate electricity consumption data and learn about the individual appliance’s power states and signatures. However fine-grained events (e.g., appliance malfunctions, abnormal power consumption, etc.) remain undetected and thus inferred contexts (such as safety hazards etc.) become invisible. In this project, we correlate an appliance’s inherent acoustic noise with its energy consumption pattern individually and in presence of multiple appliances. Our approach helps to improve the performance of energy disaggregation algorithms and provide critical insights on appliance longevity, abnormal power consumption, consumer behavior and their everyday lifestyle activities.

AARPA: Mobile and Power-line Sensing for Fine-grained Appliance Usage and Energy Monitoring

To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from powerline measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this project, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. To further improve the accuracy of appliance level usage estimation, we have been investigating a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point.

Dataset:

Energy Disaggregation

Energy Disaggregation gives the itemized energy consumption of the appliances. Research suggested that the itemized energy consumption might help reduce 15% of total residential energy consumption. Most of the commercially available energy analytic systems are intrusive and expensive. In the US about 33 million smart meters have been deployed which gives us huge amount of data. Even if we narrow our scope to a city the number of houses are above 500000 and the utility providers only provide feedback about the cumulative monthly consumption and some comparative analytic with neighbors’ total consumption. In these cases itemized consumption and comparative analytic will prove more helpful. Our objective is to look for a scalable disaggregation algorithm for big data. The underlying assumption is appliances might have similar energy patterns and we can learn those characteristics from a set of appliances and transfer the knowledge for discovering the energy patterns in a larger set.

 People:

Nilavra Pathak (Ph.D. student), Hafiz Khan (Ph.D. student), Joseph Taylor (undergraduate student), Dr. Nirmalya Roy (MPSC director), Dr. George Karabatis (collaborator at UMBC), Dr. Aryya Gangopadhyay (collaborator at UMBC) and Dr. Archan Misra (collaborator at SMU).

 Papers:

  1. Nirmalya Roy, Nilavra Pathak, and Archan Misra. “AARPA: Combining Mobile and Power-line Sensing for Fine-grained Appliance Usage and Energy Monitoring”, in Proceedings of the IEEE International Conference on Mobile Data Management (MDM), June 2015.
  2. Nilavra Pathak, Md. Abdullah Al Hafiz Khan, and Nirmalya Roy. “Acoustic based appliance state identifications for fine grained energy analytics”, in Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), March 2015. [acceptance rate: 15%]
  3. Md. Abdullah Al Hafiz Khan, Sheung Lu, Nirmalya Roy, and Nilavra Pathak. “Demo Abstract: A Microphone Sensor based System for Green Building Applications”, in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Demonstrations (PerCom), March 2015.
  4. Nirmalya Roy, David Kleinschmidt, Joseph Taylor, and Behrooz Shirazi. “Performance of the Latest Generation Powerline Networking for Green Building Applications,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings (BuildSys), November 2013.
  5. Joseph Taylor, Nirmalya Roy, David Kleinschmidt, and Behrooz Shirazi “Demo Abstract: Performance of the Latest Generation Powerline Networking for Green Building Applications,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings Demonstrations (BuildSys), November 2013.

 Press:

Constellation Awards E2 Energy to Educate Grant to UMBC’s Information Systems Department, UMBC’s Giving Blog, April 21, 2015

COEIT Uses Constellation Energy’s Education Award For Undergraduate Research, UMBC Insights, March 27, 2014

Undergrad researchers succeed with the smart plug work, WSU News, August 12, 2013

WSU Smart Environments Summer REU Explores Green Living and Healthcare Applications, WSU News, July 11, 2013